Classical Inference of a New PCI CNpmkc for Logistic-Exponential Process Distribution

Abstract

The process capability index (PCI) has been developed as a useful instrument for measuring how well a product matches customer expectations while also assessing the performance of a process. We are aware that when the quality characteristics of the processes follow normal distribution, conventional PCIs produces superior results. However, these traditional indices could not yield reliable findings when assessing nonnormally distributed processes, which could make decision-making more difficult. In this paper, we take into account the [Formula: see text] process capacity index, which may be used for both normally distributed and nonnormally distributed processes. We have employed 10 traditional methods of estimation to estimate the PCI [Formula: see text] when the process has a logistic-exponential distribution, and the performances of these traditional estimates of the index [Formula: see text] are compared in terms of their mean squared errors through a simulation exercise. Then, we create five PCI [Formula: see text] bootstrap confidence intervals and contrast them based on their average widths and related coverage probabilities. In order to demonstrate the applicability of the suggested methods of estimation, two data sets pertaining to the electronic industries are re-analyzed.

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